Hiding sensitive association rules using central tendency
Muhammad Naeem1; Sohail Asghar1; Simon Fong2
2010-12-01
Conference Name2010 6th International Conference on Advanced Information Management and Service (IMS)
Source PublicationProc. - 6th Intl. Conference on Advanced Information Management and Service, IMS2010, with ICMIA2010 - 2nd International Conference on Data Mining and Intelligent Information Technology Applications
Pages478-484
Conference Date30 Nov.-2 Dec. 2010
Conference PlaceSeoul, South Korea
Abstract

Privacy Preserving in Data Mining (PPDM) is a process by which certain sensitive information is hidden during data mining without precise access to original dataset. Majority of the techniques proposed in the literature for hiding sensitive information are based on using Support and Confidence measures in the association rules, which suffer from limitations. In this paper we propose a novel architecture which acquired other standard statistical measures instead of conventional framework of Support and Confidence to generate association rules. Specifically a weighing mechanism based on central tendency is introduced. The proposed architecture is tested with UCI datasets to hide the sensitive association rules as experimental evaluation. A performance comparison is made between the new technique and the existing one. The new architecture generates no ghost rules with complete avoidance of failure in hiding sensitive association rules. We demonstrate that Support and Confidence are not the only measures in hiding sensitive association rules. This research is aimed to contribute to data mining areas where privacy preservation is a concern.

KeywordCentral Tendency Data Mining Privacy Preservation Sensitive Association Rules
URLView the original
Language英语
Fulltext Access
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Center of Research in Data Engineering (CORDE), Mohammad Ali Jinnah University, Islamabad, Pakistan
2.Department of Computer and Info. Sci., Faculty of Science and Technology, University of Macau, China
Recommended Citation
GB/T 7714
Muhammad Naeem,Sohail Asghar,Simon Fong. Hiding sensitive association rules using central tendency[C],2010:478-484.
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